{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from glob import glob\n",
    "import os\n",
    "from shutil import copyfile\n",
    "from torch.utils.data import Dataset\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from numpy.random import permutation\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from torchvision import transforms\n",
    "from torchvision.datasets import ImageFolder\n",
    "from torchvision.models import resnet18,resnet34\n",
    "from torchvision.models.inception import inception_v3\n",
    "from torchvision.models import densenet121\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.autograd import Variable\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_cuda = torch.cuda.is_available()\n",
    "is_cuda"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Utility functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def imshow(inp,cmap=None):\n",
    "    \"\"\"Imshow for Tensor.\"\"\"\n",
    "    inp = inp.numpy().transpose((1, 2, 0))\n",
    "    mean = np.array([0.485, 0.456, 0.406])\n",
    "    std = np.array([0.229, 0.224, 0.225])\n",
    "    inp = std * inp + mean\n",
    "    inp = np.clip(inp, 0, 1)\n",
    "    plt.imshow(inp,cmap)\n",
    "    \n",
    "class FeaturesDataset(Dataset):\n",
    "    \n",
    "    def __init__(self,featlst,labellst):\n",
    "        self.featlst = featlst\n",
    "        self.labellst = labellst\n",
    "        \n",
    "    def __getitem__(self,index):\n",
    "        return (self.featlst[index],self.labellst[index])\n",
    "    \n",
    "    def __len__(self):\n",
    "        return len(self.labellst)\n",
    "    \n",
    "def fit(epoch,model,data_loader,phase='training',volatile=False):\n",
    "    if phase == 'training':\n",
    "        model.train()\n",
    "    if phase == 'validation':\n",
    "        model.eval()\n",
    "        volatile=True\n",
    "    running_loss = 0.0\n",
    "    running_correct = 0\n",
    "    for batch_idx , (data,target) in enumerate(data_loader):\n",
    "        if is_cuda:\n",
    "            data,target = data.cuda(),target.cuda()\n",
    "        data , target = Variable(data,volatile),Variable(target)\n",
    "        if phase == 'training':\n",
    "            optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.cross_entropy(output,target)\n",
    "        \n",
    "        running_loss += F.cross_entropy(output,target,size_average=False).data[0]\n",
    "        preds = output.data.max(dim=1,keepdim=True)[1]\n",
    "        running_correct += preds.eq(target.data.view_as(preds)).cpu().sum()\n",
    "        if phase == 'training':\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "    \n",
    "    loss = running_loss/len(data_loader.dataset)\n",
    "    accuracy = 100. * running_correct/len(data_loader.dataset)\n",
    "    \n",
    "    print(f'{phase} loss is {loss:{5}.{2}} and {phase} accuracy is {running_correct}/{len(data_loader.dataset)}{accuracy:{10}.{4}}')\n",
    "    return loss,accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating PyTorch datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_transform = transforms.Compose([\n",
    "        transforms.Resize((299,299)),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For Dogs & Cats dataset\n",
    "train_dset = ImageFolder('../../chapter5/dogsandcats/train/',transform=data_transform)\n",
    "val_dset = ImageFolder('../../chapter5/dogsandcats/valid/',transform=data_transform)\n",
    "classes=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd4ks4/AoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff102703cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imshow(train_dset[150][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating data loader for training and validation datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_loader = DataLoader(train_dset,batch_size=32,shuffle=False,num_workers=3)\n",
    "val_loader = DataLoader(val_dset,batch_size=32,shuffle=False,num_workers=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Densenet 121 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "my_densenet = densenet121(pretrained=True).features\n",
    "if is_cuda:\n",
    "    my_densenet = my_densenet.cuda()\n",
    "\n",
    "for p in my_densenet.parameters():\n",
    "    p.requires_grad = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting Convolutional features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#For training data\n",
    "trn_labels = []\n",
    "trn_features = []\n",
    "\n",
    "#code to store densenet features for train dataset.\n",
    "for d,la in train_loader:\n",
    "    o = my_densenet(Variable(d.cuda()))\n",
    "    o = o.view(o.size(0),-1)\n",
    "    trn_labels.extend(la)\n",
    "    trn_features.extend(o.cpu().data)\n",
    "\n",
    "#For validation data\n",
    "val_labels = []\n",
    "val_features = []\n",
    "\n",
    "#Code to store densenet features for validation dataset. \n",
    "for d,la in val_loader:\n",
    "    o = my_densenet(Variable(d.cuda()))\n",
    "    o = o.view(o.size(0),-1)\n",
    "    val_labels.extend(la)\n",
    "    val_features.extend(o.cpu().data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating train and validation feature dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create dataset for train and validation convolution features\n",
    "trn_feat_dset = FeaturesDataset(trn_features,trn_labels)\n",
    "val_feat_dset = FeaturesDataset(val_features,val_labels)\n",
    "\n",
    "# Create data loaders for batching the train and validation datasets\n",
    "trn_feat_loader = DataLoader(trn_feat_dset,batch_size=64,shuffle=True,drop_last=True)\n",
    "val_feat_loader = DataLoader(val_feat_dset,batch_size=64)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## A Fully connected network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "class FullyConnectedModel(nn.Module):\n",
    "    \n",
    "    def __init__(self,in_size,out_size):\n",
    "        super().__init__()\n",
    "        self.fc = nn.Linear(in_size,out_size)\n",
    "\n",
    "    def forward(self,inp):\n",
    "        out = self.fc(inp)\n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82944"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trn_features[0].size(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fc_in_size = trn_features[0].size(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fc = FullyConnectedModel(fc_in_size,classes)\n",
    "if is_cuda:\n",
    "    fc = fc.cuda()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(fc.parameters(),lr=0.0001)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train and validate the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training loss is 0.057 and training accuracy is 22506/23000     97.85\n",
      "validation loss is 0.034 and validation accuracy is 1978/2000      98.9\n",
      "training loss is 0.0059 and training accuracy is 22953/23000      99.8\n",
      "validation loss is 0.028 and validation accuracy is 1981/2000     99.05\n",
      "training loss is 0.0016 and training accuracy is 22974/23000     99.89\n",
      "validation loss is 0.022 and validation accuracy is 1983/2000     99.15\n",
      "training loss is 0.00064 and training accuracy is 22976/23000      99.9\n",
      "validation loss is 0.023 and validation accuracy is 1983/2000     99.15\n",
      "training loss is 0.00043 and training accuracy is 22976/23000      99.9\n",
      "validation loss is 0.024 and validation accuracy is 1983/2000     99.15\n",
      "training loss is 0.00033 and training accuracy is 22976/23000      99.9\n",
      "validation loss is 0.024 and validation accuracy is 1984/2000      99.2\n",
      "training loss is 0.00025 and training accuracy is 22976/23000      99.9\n",
      "validation loss is 0.024 and validation accuracy is 1984/2000      99.2\n",
      "training loss is 0.0002 and training accuracy is 22976/23000      99.9\n",
      "validation loss is 0.025 and validation accuracy is 1985/2000     99.25\n",
      "training loss is 0.00016 and training accuracy is 22976/23000      99.9\n",
      "validation loss is 0.024 and validation accuracy is 1986/2000      99.3\n"
     ]
    }
   ],
   "source": [
    "\n",
    "train_losses , train_accuracy = [],[]\n",
    "val_losses , val_accuracy = [],[]\n",
    "for epoch in range(1,10):\n",
    "    epoch_loss, epoch_accuracy = fit(epoch,fc,trn_feat_loader,phase='training')\n",
    "    val_epoch_loss , val_epoch_accuracy = fit(epoch,fc,val_feat_loader,phase='validation')\n",
    "    train_losses.append(epoch_loss)\n",
    "    train_accuracy.append(epoch_accuracy)\n",
    "    val_losses.append(val_epoch_loss)\n",
    "    val_accuracy.append(val_epoch_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
